Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "33" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 37 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 35 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459852 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.108163 | 43.220761 | -0.687409 | 1.198525 | -0.429776 | 14.079413 | 0.494040 | 5.671897 | 0.8315 | 0.7208 | 0.3330 | 5.019050 | 2.500611 |
| 2459851 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459849 | RF_maintenance | 100.00% | 0.00% | 8.09% | 0.00% | 100.00% | 0.00% | -0.026323 | 19.749016 | -0.663294 | 1.531768 | -0.007086 | 5.494905 | 4.389879 | 38.338043 | 0.7387 | 0.5744 | 0.4383 | 3.556072 | 1.772807 |
| 2459848 | RF_maintenance | 100.00% | 0.00% | 9.71% | 0.00% | 100.00% | 0.00% | -0.080890 | 26.656354 | 0.162202 | 0.483734 | -0.236459 | 11.857709 | 2.236272 | 33.470215 | 0.7173 | 0.5816 | 0.4426 | 3.593917 | 1.819218 |
| 2459847 | RF_maintenance | 100.00% | 0.00% | 12.35% | 0.00% | 100.00% | 0.00% | -0.081235 | 14.070382 | 0.072034 | 0.087293 | -0.538116 | 6.469044 | 2.105206 | 10.978764 | 0.7188 | 0.4965 | 0.5074 | 4.569437 | 2.254876 |
| 2459846 | RF_maintenance | 100.00% | 0.00% | 42.74% | 0.00% | 100.00% | 0.00% | -0.604296 | 8.220417 | 0.355489 | 0.152724 | 0.575060 | 1.939649 | 1.303794 | 20.864724 | 0.8404 | 0.5331 | 0.5638 | 3.443825 | 1.903633 |
| 2459845 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.241214 | 35.501722 | 1.105762 | 1.032199 | 7.618606 | 23.310903 | 23.333147 | 69.368665 | 0.7448 | 0.5937 | 0.4483 | 7.545280 | 4.285023 |
| 2459844 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.069550 | 24.783816 | -0.981497 | 32.408073 | 1681.909845 | 836.370089 | 1101.760289 | 562.691177 | 0.0430 | 0.0363 | 0.0066 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 1.316934 | 28.722638 | 1.859513 | 1.483575 | 6.332690 | 19.960179 | 21.742261 | 59.911646 | 0.7417 | 0.5842 | 0.4685 | 3.772808 | 2.139473 |
| 2459839 | RF_maintenance | 100.00% | - | - | - | - | - | 13.678626 | 13.249021 | 83.662344 | 92.397749 | nan | nan | -29.231224 | -30.920487 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.230366 | 21.992069 | 1.729238 | 1.021840 | 0.236156 | 12.135532 | 1.221293 | 17.878411 | 0.0657 | 0.0979 | 0.0325 | 1.276291 | 1.282107 |
| 2459836 | RF_maintenance | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0780 | 0.0502 | 0.0090 | nan | nan |
| 2459835 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.538552 | 0.336472 | -0.895952 | 2.157077 | 0.127977 | 8.199165 | 0.174644 | 8.075354 | 0.0794 | 0.0533 | 0.0112 | nan | nan |
| 2459833 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.259240 | 6.160997 | 0.652759 | 6.016596 | -0.952992 | 21.918035 | 1.091957 | 19.120911 | 0.0555 | 0.0421 | 0.0030 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.572382 | 14.612434 | 1.668769 | 0.836484 | -0.866316 | 2.077490 | 0.469892 | 25.721668 | 0.7463 | 0.2800 | 0.6208 | 3.506536 | 1.987249 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 0.641921 | 3.733518 | -0.290408 | 4.036640 | -0.148712 | -0.981293 | 0.798235 | 2.931446 | 0.0477 | 0.0454 | 0.0056 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.315978 | 12.200231 | 2.579915 | 1.948918 | 52.528853 | 50.646198 | 200.737979 | 217.748397 | 0.0518 | 0.0945 | 0.0414 | 1.474992 | 1.449167 |
| 2459829 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.258748 | 24.049209 | 2.744191 | 1.454863 | 50.543811 | 44.889318 | 529.934265 | 487.644568 | 0.0678 | 0.0974 | 0.0305 | -0.000000 | -0.000000 |
| 2459828 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.197193 | 9.774033 | 0.906625 | -0.278314 | 32.250918 | 29.828482 | 277.936963 | 293.875855 | 0.0543 | 0.0929 | 0.0373 | 14.127906 | 8.007195 |
| 2459827 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.473730 | 16.434176 | -0.247819 | 0.239853 | 30.754036 | 27.562967 | 113.971115 | 111.336200 | 0.0542 | 0.0964 | 0.0304 | 1.336757 | 1.322387 |
| 2459826 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.131589 | 7.761617 | -0.523185 | -0.299241 | 34.092924 | 35.347631 | 148.911144 | 182.185848 | 0.0532 | 0.0751 | 0.0208 | 1.419335 | 1.401785 |
| 2459825 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.668704 | 9.090731 | -0.400867 | -0.552544 | -0.750979 | 2.506798 | 0.571559 | 4.549659 | 0.0620 | 0.0940 | 0.0356 | 1.444847 | 1.434198 |
| 2459824 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.137791 | 20.973890 | -0.592486 | 0.136329 | 54.701229 | 47.003557 | 194.542899 | 186.894231 | 0.0640 | 0.0991 | 0.0302 | 1.632441 | 1.642507 |
| 2459823 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459822 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 46.559323 | 46.141493 | inf | inf | 3894.459793 | 3894.106748 | 5147.013472 | 5146.487106 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459821 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.037376 | 6.794832 | -0.273011 | -0.347856 | -0.957320 | 1.109534 | -1.412192 | 1.493045 | 0.0592 | 0.0979 | 0.0338 | 1.213487 | 1.213507 |
| 2459820 | RF_maintenance | 100.00% | 0.00% | 26.32% | 0.00% | 100.00% | 0.00% | 0.604021 | 26.550909 | -0.090166 | 0.201111 | 64.297868 | 63.113903 | 259.725293 | 250.501822 | 0.7509 | 0.4731 | 0.5368 | 4.990405 | 2.235443 |
| 2459817 | RF_maintenance | 100.00% | 0.00% | 54.30% | 0.00% | 100.00% | 0.00% | 0.987737 | 5.397661 | 0.947301 | -0.182506 | 3.847800 | 7.542041 | 8.944943 | 15.462332 | 0.7980 | 0.4827 | 0.6134 | 3.776736 | 2.124954 |
| 2459816 | RF_maintenance | 100.00% | 0.00% | 80.19% | 0.00% | 100.00% | 0.00% | 0.224575 | 8.594751 | 1.111999 | -0.530777 | 33.488412 | 28.954821 | 204.015214 | 197.490950 | 0.8402 | 0.4059 | 0.6842 | 3.974793 | 2.069556 |
| 2459815 | RF_maintenance | 100.00% | 0.00% | 51.61% | 0.00% | 100.00% | 0.00% | 0.653517 | 3.916170 | 0.900670 | -0.583486 | 16.867358 | 16.309160 | 138.636285 | 147.395397 | 0.7883 | 0.4923 | 0.6178 | 4.633252 | 2.180501 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | 43.220761 | -0.108163 | 43.220761 | -0.687409 | 1.198525 | -0.429776 | 14.079413 | 0.494040 | 5.671897 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 38.338043 | -0.026323 | 19.749016 | -0.663294 | 1.531768 | -0.007086 | 5.494905 | 4.389879 | 38.338043 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 33.470215 | 26.656354 | -0.080890 | 0.483734 | 0.162202 | 11.857709 | -0.236459 | 33.470215 | 2.236272 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | 14.070382 | 14.070382 | -0.081235 | 0.087293 | 0.072034 | 6.469044 | -0.538116 | 10.978764 | 2.105206 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 20.864724 | -0.604296 | 8.220417 | 0.355489 | 0.152724 | 0.575060 | 1.939649 | 1.303794 | 20.864724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 69.368665 | 35.501722 | 0.241214 | 1.032199 | 1.105762 | 23.310903 | 7.618606 | 69.368665 | 23.333147 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Variability | 1681.909845 | -0.069550 | 24.783816 | -0.981497 | 32.408073 | 1681.909845 | 836.370089 | 1101.760289 | 562.691177 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 59.911646 | 28.722638 | 1.316934 | 1.483575 | 1.859513 | 19.960179 | 6.332690 | 59.911646 | 21.742261 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Power | 92.397749 | 13.249021 | 13.678626 | 92.397749 | 83.662344 | nan | nan | -30.920487 | -29.231224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | 21.992069 | 21.992069 | 0.230366 | 1.021840 | 1.729238 | 12.135532 | 0.236156 | 17.878411 | 1.221293 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Variability | 8.199165 | 0.336472 | -0.538552 | 2.157077 | -0.895952 | 8.199165 | 0.127977 | 8.075354 | 0.174644 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Variability | 21.918035 | 6.160997 | -0.259240 | 6.016596 | 0.652759 | 21.918035 | -0.952992 | 19.120911 | 1.091957 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 25.721668 | 0.572382 | 14.612434 | 1.668769 | 0.836484 | -0.866316 | 2.077490 | 0.469892 | 25.721668 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Power | 4.036640 | 0.641921 | 3.733518 | -0.290408 | 4.036640 | -0.148712 | -0.981293 | 0.798235 | 2.931446 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 217.748397 | 1.315978 | 12.200231 | 2.579915 | 1.948918 | 52.528853 | 50.646198 | 200.737979 | 217.748397 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Discontinuties | 529.934265 | 24.049209 | 0.258748 | 1.454863 | 2.744191 | 44.889318 | 50.543811 | 487.644568 | 529.934265 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 293.875855 | 9.774033 | 0.197193 | -0.278314 | 0.906625 | 29.828482 | 32.250918 | 293.875855 | 277.936963 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Discontinuties | 113.971115 | 0.473730 | 16.434176 | -0.247819 | 0.239853 | 30.754036 | 27.562967 | 113.971115 | 111.336200 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 182.185848 | 7.761617 | 0.131589 | -0.299241 | -0.523185 | 35.347631 | 34.092924 | 182.185848 | 148.911144 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | 9.090731 | 9.090731 | 0.668704 | -0.552544 | -0.400867 | 2.506798 | -0.750979 | 4.549659 | 0.571559 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Discontinuties | 194.542899 | 0.137791 | 20.973890 | -0.592486 | 0.136329 | 54.701229 | 47.003557 | 194.542899 | 186.894231 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Power | inf | 46.559323 | 46.141493 | inf | inf | 3894.459793 | 3894.106748 | 5147.013472 | 5146.487106 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | 6.794832 | 6.794832 | 0.037376 | -0.347856 | -0.273011 | 1.109534 | -0.957320 | 1.493045 | -1.412192 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Discontinuties | 259.725293 | 0.604021 | 26.550909 | -0.090166 | 0.201111 | 64.297868 | 63.113903 | 259.725293 | 250.501822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 15.462332 | 0.987737 | 5.397661 | 0.947301 | -0.182506 | 3.847800 | 7.542041 | 8.944943 | 15.462332 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | ee Temporal Discontinuties | 204.015214 | 8.594751 | 0.224575 | -0.530777 | 1.111999 | 28.954821 | 33.488412 | 197.490950 | 204.015214 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Temporal Discontinuties | 147.395397 | 3.916170 | 0.653517 | -0.583486 | 0.900670 | 16.309160 | 16.867358 | 147.395397 | 138.636285 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | N02 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |